A causal approach for mining interesting anomalies

Sakshi Babbar, Didi Surian, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We propose a novel approach which combines the use of Bayesian network and probabilistic association rules to discover and explain anomalies in data. The Bayesian network allows us to organize information in order to capture both correlation and causality in the feature space, while the probabilistic association rules have a structure similar to association mining rules. In particular, we focus on two types of rules: (i) low support & high confidence and, (ii) high support & low confidence. New data points which satisfy either one of the two rules conditioned on the Bayesian network are the candidate anomalies. We perform extensive experiments on well-known benchmark data sets and demonstrate that our approach is able to identify anomalies in high precision and recall. Moreover, our approach can be used to discover contextual information from the mined anomalies, which other techniques often fail to do so.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Proceedings
Pages226-232
Number of pages7
DOIs
StatePublished - 2013
Externally publishedYes
Event26th Canadian Conference on Artificial Intelligence, Canadian AI 2013 - Regina, SK, Canada
Duration: May 28 2013May 31 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7884 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th Canadian Conference on Artificial Intelligence, Canadian AI 2013
Country/TerritoryCanada
CityRegina, SK
Period05/28/1305/31/13

Keywords

  • Bayesian network
  • anomaly
  • causality
  • probabilistic association rules

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